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We are looking for a Lead AI Engineer to help shape and drive AI/ML enablement and readiness across the organization. This role requires strong data engineering fundamentals: you will start hands-on, contributing directly to our data platform and pipelines, while progressively taking on a leading role in defining how the organization builds, deploys, and governs AI/ML capabilities.
Reporting directly to the VP, Head of Data, you will work autonomously to identify gaps, propose solutions, and bring innovative thinking to how our data and AI/ML ecosystem should evolve. You will partner closely with Data Governance, Data Engineering, and Product stakeholders to define our AI/ML frameworks and MLOps strategy, and to ensure the organization is well-positioned to adopt AI/ML responsibly and at scale.
Key Accountabilities/Deliverables:
Design, build, and optimize data pipelines, ingestion frameworks, and platform components that support analytics, reporting, and AI/ML use cases.
Take direct, autonomous ownership of complex engineering initiatives, from technical design through implementation and rollout, with minimal need for oversight.
Identify and resolve performance, scalability, and reliability issues across the existing data platform.
Bring innovative, well-reasoned solutions to data engineering problems, proactively identifying gaps and proposing improvements rather than waiting for direction.
Write clean, well-tested, well-documented code and infrastructure-as-code, maintaining strong engineering hygiene across your work.
Help define the organization's AI/ML frameworks, evaluating and recommending tools, platforms, and standards for building and deploying AI/ML solutions.
Build working prototypes that provide immediate value to the engineering teams
Shape and help implement our MLOps strategy, including approaches to model deployment, monitoring, versioning, and lifecycle management
Partner in deep, ongoing collaboration with Data Governance to ensure AI/ML frameworks and practices align with data governance, security, and compliance standards.
Design and advocate for data infrastructure patterns that support AI/ML use cases at scale (e.g., feature stores, curated/governed datasets, streaming access for training and inference).
Partner with Data Science, Data Engineering, and business stakeholders to assess AI/ML readiness gaps and build a roadmap to close them.
Act as a subject-matter expert and thought partner to the VP, Head of Data on emerging AI/ML technologies, practices, and industry trends.
Document AI/ML standards, frameworks, and decisions to support consistent adoption across the organization as the practice matures.
Act as a senior technical resource for the team, providing guidance on architecture, design patterns, and best practices AI/ML readiness and ML Ops frameworks
Partner closely with Enterprise Architecture on establishing architectural blueprints for AI readiness
Other Duties as Assigned.
Technical Knowledge and Understanding:
Data Engineering
Strong data engineering fundamentals: deep expertise in data pipeline design, optimization, and distributed data processing (e.g., Spark, dbt, Airflow, Kafka, or equivalent).
Platforms: hands-on experience with Snowflake, Databricks, and/or Azure Synapse Analytics, with the ability to architect and optimize workloads on one or more of these platforms.
Strong knowledge of cloud platforms (AWS, Azure, or GCP) and modern data warehouse/lakehouse architectures.
AI/ML Engineering
Programming & software engineering fundamentals
Strong Python (the de facto language for AI/ML tooling); solid software engineering practices (testing, version control, code review) since AI engineers ship production systems, not just notebooks
API design and integration — most AI engineering work today is building systems around models (orchestration, tool-calling, retrieval), not training them from scratch
LLM & foundation model fluency
Practical experience with LLM APIs (ie. OpenAI) and open-weight models
Prompt engineering and prompt evaluation as a discipline, not just trial-and-error
Understanding of context windows, tokenization, embeddings, and model limitations (hallucination, latency, cost tradeoffs)
RAG (Retrieval-Augmented Generation) & data retrieval
Vector databases (Pinecone, Weaviate, pgvector, etc.) and embedding models
Chunking strategies, hybrid search, reranking
Agentic systems & orchestration
Frameworks like LangChain, LangGraph, LlamaIndex, or custom orchestration
Tool-use / function-calling design, multi-step reasoning chains, agent memory and state management
Fine-tuning & model adaptation
When to fine-tune vs. prompt vs. RAG
Familiarity with parameter-efficient methods (LoRA, etc.) MLOps / LLMOps
Model evaluation frameworks, A/B testing for model outputs, observability (tracing, logging model calls)
Deployment patterns: latency/cost optimization, caching, streaming responses, fallback handling
Versioning prompts and models, not just code
Safety, evaluation & governance awareness
Bias/safety evaluation, guardrails, handling PII appropriately
Experience:
Minimum 7+years of experience in data engineering, with experience working on large-scale, mature data platforms.
3+ years of experience developing ML or AI deliverables – includes deployment to production
Bachelor's degree in related field or demonstrated equivalent experience in a related field required.
Working knowledge of Agentic Workflows for engineering and architecture
Demonstrated experience taking autonomous technical ownership of complex projects from design through delivery, with minimal oversight.
Experience contributing to or shaping AI/ML enablement efforts, such as defining frameworks, evaluating MLOps tooling, or building infrastructure that supports model training and deployment.
Experience partnering with Data Governance, Data Science, or Compliance teams to align technical practices with governance and regulatory requirements.
A track record of proposing and driving innovative technical solutions rather than simply executing predefined plans.
Experience designing or implementing Agentic workflows for data engineering preferred.
Experience working with Property & Casualty insurance carriers preferred.
Experience with Data Vault 2.0 or Ensemble data modeling techniques preferred.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over work authorization sponsorship now or in the future for this position.
#LI-Hybrid
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At Core Specialty, you will receive a competitive salary and opportunities for professional development and advancement. We offer medical, dental, vision, and life insurances; short and long-term disability; a Company-match of 100% of a 6% contribution 401(k) plan; an Employee Assistance Plan; Health Savings Account, Flexible Spending Account, Health Reimbursement Account, and a wellness program
Skills Required
- Minimum 7 years of experience in data engineering, including work on large-scale, mature data platforms
- At least 3 years of experience developing and deploying ML or AI deliverables to production
- Bachelor’s degree in a related field or equivalent demonstrated experience
- Strong data engineering fundamentals, including pipeline design, optimization, and distributed data processing
- Experience with Snowflake, Databricks, and/or Azure Synapse Analytics
- Strong knowledge of AWS, Azure, or GCP and modern data warehouse or lakehouse architectures
- Strong Python and software engineering practices, including testing, version control, and code review
- Experience with LLM APIs, open-weight models, prompt engineering, embeddings, vector databases, RAG, and agentic orchestration
- Working knowledge of agentic workflows for engineering and architecture
- Experience with MLOps or LLMOps, including deployment, monitoring, evaluation, versioning, observability, and lifecycle management
- Experience taking autonomous technical ownership of complex projects from design through delivery
- Experience contributing to AI/ML enablement, framework definition, MLOps tooling evaluation, or model infrastructure
- Experience partnering with Data Governance, Data Science, or Compliance teams on governance and regulatory requirements
- Experience designing or implementing agentic workflows for data engineering
- Experience working with Property and Casualty insurance carriers
- Experience with Data Vault 2.0 or Ensemble data modeling techniques
- Authorization to work for any employer in the United States without current or future sponsorship
What We Do
Core Specialty, through its subsidiary insurers, offers a diversified range of property, casualty, and marine insurance products for small to mid-sized businesses. We have the capital to take on risk, the underwriting talent in place, decisive leadership team, infrastructure, and a proven track record of making things happen – fast! When you’re ready to solve your toughest insurance needs, we’re ready to get it done for you. We free customers up to focus on their business by taking the load of complicated specialty insurance off their hands. We break down the walls of bureaucracy to provide optimal underwriting solutions for brokers. We’re ready, equipped, and motivated to get the job done, efficiently and professionally, by empowering experts with what they need to move quickly on behalf of customers and their brokers. Our specialty focus is the essential part of our identity. It is at our core
